Linganisha mbinu

	Uchanganuzi wa nguzo wa kihierarkia wa Kibayes (BHC)	Uundaji wa Mchanganyiko wa Kibayesiani
Nyanja	Takwimu	Takwimu
Familia	Latent structure	Latent structure
Mwaka wa asili	2005	1997 (Richardson & Green Bayesian formulation)
Mwanzilishi	Katherine Heller & Zoubin Ghahramani	Richardson & Green (seminal Bayesian treatment, 1997); broader Bayesian mixture roots trace to Dempster, Laird & Rubin (EM, 1977) and Titterington, Smith & Makov (1985)
Aina	Probabilistic clustering / model-based hierarchical agglomeration	Latent-class / model-based clustering
Chanzo asilia	Heller, K. A. & Ghahramani, Z. (2005). Bayesian hierarchical clustering. In Proceedings of the 22nd International Conference on Machine Learning (ICML 2005), pp. 297–304. ACM.	Fruhwirth-Schnatter, S., Celeux, G. & Robert, C. P. (Eds.) (2019). Handbook of Mixture Analysis. CRC Press / Chapman & Hall. ISBN: 9780367733995
Majina mbadala	BHC, probabilistic hierarchical clustering, Bayesian agglomerative clustering	Bayesian mixture model, BMM, Bayesian model-based clustering, Bayesian finite mixture
Zinazohusiana	6	4
Muhtasari	Bayesian hierarchical clustering is a probabilistic agglomerative algorithm that builds a tree of nested cluster merges using Bayesian model comparison at each step. Rather than minimising a geometric linkage criterion, it evaluates at every candidate merge whether the data from two clusters are better explained by a single combined model or by two separate models, yielding a statistically principled dendrogram.	Bayesian mixture modeling represents the population as a weighted sum of K component distributions and estimates all unknowns — mixing weights, component parameters, and even the number of components — through posterior inference. It extends classical mixture analysis by placing priors on every parameter and quantifying uncertainty over latent group assignments rather than treating them as fixed.